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Gambling Model Analysis: The Revenue Strategy and User Rise of Encryption Games from GGR to NGR
The Application of Betting Models in Encryption Games: Revenue Strategies from GGR to NGR
Before starting a new encryption gaming project or considering an investment, it is crucial to understand the GGR model in the gambling industry. GGR (Gross Gaming Revenue) is the most commonly used performance metric in the gambling industry, calculated as total bets minus total payouts (the amount won by players). Another important metric is NGR (Net Gaming Revenue), calculated as total bets minus total payouts, total bonus payouts, and total gaming tax payouts.
GGR has another calculation method: total bets multiplied by the house edge. The house edge refers to the percentage of total bets that the dealer ultimately wins. Even in relatively fair games, such as blackjack, baccarat, or dice games, there exists a house advantage ranging from 0.5% to 20%.
The house hopes that players will continue to place bets and increase their bet amounts. Therefore, in online gambling, the actual house edge is often controlled at a balanced point: it cannot let the kill rate be too low to affect profits, nor can it let the kill rate be too high to lead to player loss. The key is to provide players with certainty in odds and to reach a consensus on this.
The main difference between encryption games and traditional gambling lies in whether players can accept variable odds. The odds framework in traditional gambling games is relatively fixed, while in cryptocurrency trading, the odds of different tokens can vary significantly at different times.
The gambling industry focuses on consensus mechanisms and odds frameworks, with its target audience primarily seeking an advantage within fixed rules. This does not completely overlap with the characteristics of cryptocurrency speculators or traders. The gambling industry does not require a wide variety of games, but rather a widely adopted mechanism.
In terms of controlling the kill rate, there are fundamental differences between gambling, contract trading, and encryption currency trading:
The less fixed the odds are, the greater the liquidity of the chips, making it more difficult to control the kill rate. This also explains why most games, whether they belong to the gambling category or not, do not adopt an absolute true random mechanism.
The kill rate directly affects the lifecycle GGR of players' units. From gambling to cryptocurrency trading, user stickiness is gradually decreasing, which directly impacts user growth and conversion logic.
In terms of growth strategy, the gambling industry commonly uses the concepts of "mud code" and "washing code". Mud code is only used for betting and cannot be redeemed, while cash code can be exchanged in both directions. Each bet is a wash code, and intermediaries can receive commissions, and even players can receive rebates. This mechanism is similar to the commission system of contract exchanges.
The mud code locks in liquidity, directly promoting betting (trading) behavior. Wash code and rebate essentially form a consistency of interests that creates key growth nodes; the larger the turnover, the more profit is made, while also ensuring the controllability of the kill rate, thus improving the GGR per unit lifecycle.
For encryption game teams, it is important to recognize the differences between encryption players and traditional game players. They do not simply consume for entertainment; rather, they expect returns. Therefore, game design should fully consider the gambling model, determine a suitable GGR strategy for the project, and develop a reasonable revenue model and kill rate control methods. At the same time, strategies for customer acquisition and retention from traditional gambling industries can be referenced to optimize user experience.